Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · q-bio/0610025

Function Constrains Network Architecture and Dynamics: A Case Study on the Yeast Cell Cycle Boolean Network

Kai-Yeung Lau·Surya Ganguli·Chao Tang
View original source ↗Read source document ↗Export metadata ↓

Abstract

We develop a general method to explore how the function performed by a biological network can constrain both its structural and dynamical network properties. This approach is orthogonal to prior studies which examine the functional consequences of a given structural feature, for example a scale free architecture. A key step is to construct an algorithm that allows us to efficiently sample from a maximum entropy distribution on the space of boolean dynamical networks constrained to perform a specific function, or cascade of gene expression. Such a distribution can act as a "functional null model" to test the significance of any given network feature, and can aid in revealing underlying evolutionary selection pressures on various network properties. Although our methods are general, we illustrate them in an analysis of the yeast cell cycle cascade. This analysis uncovers strong constraints on the architecture of the cell cycle regulatory network as well as significant selection pressures on this network to maintain ordered and convergent dynamics, possibly at the expense of sacrificing robustness to structural perturbations.

Explore related subjects

q-bio.MN

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kai-Yeung Lau, Surya Ganguli, Chao Tang. 2007-04-24. Function Constrains Network Architecture and Dynamics: A Case Study on the Yeast Cell Cycle Boolean Network. https://doi.org/10.1103/physreve.75.051907

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Accelerating reaction network identification via frequency-domain analysis

The identification of intracellular reaction networks from single-cell data lies at the heart of many biological studies, as it can unravel key mechanisms in living organisms and provide insights for their rational engineering. However, the stochastic and nonlinear nature of these reacting systems poses significant challenges for this identification problem, making the state-of-the-art methods (e.g., particle-filtering-based approaches) computationally demanding. This paper reports a noteworthy numerical observation that for certain systems, the Fourier transform can render the likelihood of the measurement data approximately Gaussian, therefore resulting in a substantially accelerated Bayesian inference algorithm. A gene transcription example is presented to illustrate this finding and demonstrate the efficiency of {the proposed Fourier-based method}.

q-bio.MN↗
arXiv2026

Can a self-sustaining chemical network emerge due to just a few catalysts?

Understanding how self-sustaining chemical reaction networks can emerge from simple molecules is an important question in chemical approaches to the origin of life. Reflexively autocatalytic and food-generated (RAF) sets provide a mathematical framework for investigating this question. This line of research goes back to the early work of Kauffman on autocatalytic reaction networks and was subsequently developed through the probabilistic framework of Mossel and Steel. Further contributions by Hordijk, Steel, and collaborators extended the theory to more heterogeneous forms of catalysis, including reaction-dependent probabilities or power-law distributions of catalytic activity. We introduce a stochastic model that combines heterogeneous molecular catalytic activity with reaction-dependent molecule-reaction compatibility. By assigning random catalytic weights to molecules and incorporating compatibility through an explicit formulation of catalysis probabilities, our model provides a concrete and flexible setting that captures both sources of heterogeneity while remaining mathematically tractable. We establish novel and general criteria for RAF formation and nonformation. We further demonstrate that classical phase-transition criteria need not remain valid in the presence of reaction-dependent catalysis. For heavy-tailed catalytic weights, we identify and rigorously quantify an additional phase transition. In models with finite-mean catalytic weights, RAF formation and nonformation are governed by a catalytic intensity parameter. For infinite-mean weights, however, we show that RAFs may emerge with probability tending to one even when the catalytic intensity parameter tends to zero.

q-bio.MN↗
arXiv2026

Modeling Gene Expression Noise: From Transcriptional Dynamics to Cell Growth and Division

Gene expression is inherently noisy, leading to substantial cell-to-cell variation in mRNA and protein levels. The telegraph model and the simpler bursty model provide good fits to single-cell count distributions and have been used extensively to understand transcriptional kinetics and estimate the kinetic parameters. However, these models all describe a static cell. Here, we review models of stochastic gene expression in growing and dividing cells which aim to understand how a cell's own life history, from its birth, through copying its genome, to its division, leaves a distinct fluctuation signature on molecule numbers. We discuss the biochemical and biophysical processes captured by these models, their analytical tractability, the concept of cyclo-stationary behavior, when this is well approximated by the steady-state of static models and how the distributions of molecular numbers measured under these conditions sensitively depend on the experimental setup. This is followed by a discussion of how these models are fitted to single-cell measurements, how biological variability can be separated from measurement noise, and which computational advances make this feasible for thousands of genes. We finish by highlighting the open problems in the field.

q-bio.MN↗